Benchmarking and Experimental Validation of a Real-Time Multi-Robot Hybrid Coverage Algorithm for Known Environments

Abstract

In the context of asset inspection, the size of the environment to be covered can be large. Mobile robotic systems are capable of acquiring more extensive data than static sensors, but the capacity of the robotic platform used can be limited by its autonomy and sensing capabilities. This is why multi-robot systems are interesting in such applications. However, scaling up to larger robot team sizes requires coordination among robots to be carried out efficiently. In this work, we investigate a hybrid coordination strategy with a team of micro-aerial vehicles, where a ground station centrally assigns tasks in real time to the robots, and the robots distributively coordinate their trajectories to carry out the coverage of a known asset. In particular, we perform the benchmarking and experimental validation of such a strategy. Several variants of the strategy are implemented by adapting existing state-of-the-art solutions to this context. Extensive simulation experiments are carried out in various environments to benchmark each variant and evaluate how their performance scales with the robot team size. The results show that the strategy scales well for larger robot teams, thanks to its efficient task generation process. Notably, despite its relatively simple but efficient task generation technique, it outperforms or is comparable to other methods employing more complex schemes (such as information gain or frontiers). Finally, we validated the proposed strategy with teams of up to three robots in physical experiments.